International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
184 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Application of Forecasting Methods for the Estimation of Production Demand
By
1Ezeliora Chukwuemeka Daniel
1Department of Industrial and Production Engineering, Nnamdi Azikiwe University Awka, Anambra
State, Nigeria Mobile: 2348060480087, 2Umeh Maryrose Ngozi
2Department of Computer Science, Nnamdi Azikiwe University Awka, Anambra State, Nigeria; Mobile:
2348050495912; 3Mbeledeogu Njide N.
3Department of Computer Science, Nnamdi Azikiwe University Awka, Anambra State, Nigeria;
4Okoye Ugochukwu Patrick
4Department of Chemical Engineering, Nnamdi Azikiwe University Awka, Anambra State, Nigeria
Mobile: 2348032902484,
Abstract: The researcher applied forecasting method to analyze the production demand in
millennium plastic industry. The data were analyzed using double exponential smoothing and
winters methods to see if the products were going to either decreasing or increasing in future
demand. This technique will help during production planning.
Key words: Forecasting, Production Planning, Production Demand Dust Pan and Paint Bucket
Introduction to Forecasting
Forecasting is the process of making statements about events whose actual outcomes (typically)
have not yet been observed. A commonplace example might be estimation of some variable of
interest at some specified future date. Prediction is a similar, but more general term. Both might
refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or
alternatively to less formal judgmental methods. Usage can differ between areas of application:
for example, in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for
estimates of values at certain specific future times, while the term "prediction" is used for more
general estimates, such as the number of times floods will occur over a long period.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good
practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be
up to date in order for the forecast to be as accurate as possible.[1]
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
185 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Objective of the study is the use of historical data to foreseen the future. This was accomplish by
the use of forecasting tools
Categories of Forecasting Methods: Qualitative forecasting techniques are subjective, based
on the opinion and judgment of consumers, experts; they are appropriate when past data are not
available. They are usually applied to intermediate- or long-range decisions. Examples of
qualitative forecasting methods are informed opinion and judgment, the Delphi method, market
research, and historical life-cycle analogy.
Quantitative forecasting models are used to forecast future data as a function of past data; they
are appropriate when past data are available. These methods are usually applied to short- or
intermediate-range decisions. Examples of quantitative forecasting methods are last period
demand, simple and weighted N-Period moving averages, simple exponential smoothing, and
multiplicative seasonal indexes.
Naïve approach: Naïve forecasts are the most cost-effective objective forecasting model, and
provide a benchmark against which more sophisticated models can be compared. For stationary
time series data, this approach says that the forecast for any period equals the historical average.
For time series data that are stationary in terms of first differences, the naïve forecast equals the
previous period's actual value.
Time series methods: Time series methods use historical data as the basis of estimating future
outcomes.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
186 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Causal / Econometric Forecasting Methods: Some forecasting methods use the assumption that
it is possible to identify the underlying factors that might influence the variable that is being
forecast. For example, including information about climate patterns might improve the ability of a
model to predict umbrella sales. This is a model of seasonality which shows a regular pattern of
up and down fluctuations. In addition to climate, seasonality can also be due to holidays and
customs; for example, one might predict that sales of college football apparel will be higher
during the football season than during the off season.[2]
Causal forecasting methods are also subject to the discretion of the forecaster. There are several
informal methods which do not have strict algorithms, but rather modest and unstructured
guidance. Alternatively, one can forecast based on, for example, linear relationships. If one
variable is linearly related to the other for a long enough period of time, it may be beneficial to
extrapolate such a relationship into the future.
Causal methods include:
Regression analysis includes a large group of methods that can be used to predict future
values of variable using information about other variables. These methods include both
parametric (linear or non-linear) and non-parametric techniques.
Autoregressive moving average with exogenous inputs (ARMAX)[3]
Quantitative forecasting models are often judged against each other by comparison of their in-
sample or out-of-sample mean square error, although some researchers have advised against its
use.[4]
Judgmental Methods: Judgmental forecasting methods incorporate intuitive judgments, opinions
and subjective probability estimates.
Demand Forecasting: demand forecasting is the activity of estimating the quantity of a product
or service that consumers will purchase. Demand forecasting involves techniques including both
informal methods, such as educated guesses, and quantitative methods, such as the use of
historical sales data or current data from test markets. Demand forecasting may be used in
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
187 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
making pricing decisions, in assessing future capacity requirements, or in making decisions on
whether to enter a new market.
Importance of Demand Forecasting: Production Planning and product scheduling a business
firm cannot function in wilderness. It has to take crucial decisions about what to produce and
how much to produce. This in return depends upon its estimate of future demand for the product.
If the forecasted demand is likely to rise, the firm can plan expansion of its production
capabilities to meet the growing demand at the right point of time. In the eventuality of declining
demand
Inventory planning Demand forecasting is useful for the firm to acquire the right quantum of
inventory at the right point of time, to meet the needs of the production same time without
unnecessarily locking up the finances of the firm in inventory accumulation.
Capital planning: Increased production requires increased capital resources fixed as well as
working capital. Availability of demand forecasts helps the firm to mobilize the capital resources
in time.
Marketing strategy: Demand forecasting will be useful in devising appropriate sales promotion
or marketing strategies. If the demand forecasts indicate a declining trend in sales, it should
resort to intensive sales promotion campaign to sustain its sales.
Manpower planning: A firm has to recruit and train the appropriate level of work force. This
calls for forecasting the demand well in advance so that the required contingent of the labor
resources could be obtained.
Pricing strategies: Devising and setting the optimum pricing depends upon the forecasted
demand. If the forecasts indicate a declining share in the market demand then it has to slash the
prices to sustain demand. Conversely, if the forecasts indicate increased demand for the prod act
over a longer period it can charge higher prices subject to the other considerations. [15]
Components of Demand Forecasting: There are two main factors that help determine the type
of forecasting method that will be used. They are: Time Frame and Behavior.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
188 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Time Frame: The length of the forecast depends on product market changes and susceptibility
to technological changes. The classifications are generalizations. Short to Mid and Long range
is all relative to the business and what is being forecast.
Short to Mid-Range forecasts are usually anywhere from daily to up to two years in length. They
are commonly used to determine production and delivery schedules and to establish inventory
levels.
Long-Range forecasts are generally over two years into the future. They are usually used for
strategic planning. Strategic planning determines where the company is headed in the future. It
is used to establish long-term goals, plan new products, enter new markets and develop new
facilities & technology.
Behavior: Demand sometimes behaves in random and irregular ways. Other times it exhibits
predictable behavior. The 3 main types of predictable behavior are trends, cycles, and seasonal
patterns.
A trend is a gradual, long-term, upward or downward movement in demand. A current trend is
the steady increase in sales of personal computers over the past few years.
A cycle is an up-and-down movement in demand that repeats itself over a longer time span.
Automotive sales often behave in a cyclical pattern.
Application of Forecasting: The process of climate change and increasing energy prices has led
to the usage of Egain Forecasting of buildings. The method uses forecasting to reduce the energy
needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used
in the practice of Customer Demand Planning in everyday business forecasting for
manufacturing companies. Forecasting has also been used to predict the development of conflict
situations. Experts in forecasting perform research that use empirical results to gauge the
effectiveness of certain forecasting models.[5] Research has shown that there is little difference
between the accuracy of forecasts performed by experts knowledgeable of the conflict situation
of interest and that performed by individuals who knew much less.[6]
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
189 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Similarly, experts in some studies argue that role thinking does not contribute to the accuracy of
the forecast.[7] The discipline of demand planning, also sometimes referred to as supply chain
forecasting, embraces both statistical forecasting and a consensus process. An important, albeit
often ignored aspect of forecasting, is the relationship it holds with planning. Forecasting can be
described as predicting what the future will look like, whereas planning predicts what the future
should look like.[8][9] There is no single right forecasting method to use. Selection of a method
should be based on your objectives and your conditions (data etc.).[10] A good place to find a
method is by visiting a selection tree. [11]
Forecasting has application in many situations:
Supply chain management - Forecasting can be used in Supply Chain Management to
make sure that the right product is at the right place at the right time. Accurate
forecasting will help retailers reduce excess inventory and therefore increase profit
margin. Studies have shown that extrapolations are the least accurate, while company
earnings forecasts are the most reliable.[12] Accurate forecasting will also help them
meet consumer demand.
Limitations Forecasting Methods: Limitations pose barriers beyond which forecasting methods
cannot reliably predict.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
190 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Performance limits of fluid dynamics equations: As proposed by Edward Lorenz in 1963, long
range weather forecasts, those made at a range of two weeks or more, are impossible to
definitively predict the state of the atmosphere, owing to the chaotic nature of the fluid dynamics
equations involved. Extremely small errors in the initial input, such as temperatures and winds,
within numerical models double every five days.[13]
Complexity introduced by the technological singularity: The technological singularity is the
theoretical emergence of super intelligence through technological means.[14] Since the
capabilities of such intelligence would be difficult for an unaided human mind to comprehend, the
technological singularity is seen as an occurrence beyond which events cannot be predicted.
Ray Kurzweil predicts the singularity will occur around 2045 while Vernor Vinge predicts it will
happen sometime before 2030.
Research Methodology: The two forecasting methods adopted were double exponential
smoothing and winters method. The methods were used to forecast the production demand of the
case study company. This will help to understand what their future production demand will look
like
Table 1: Production Demand in a Millenium Manufacturing Industry Products 20 litres Paint Bucket 4 litres Paint Bucket Dust Pan (Parker)
Year /Month
2010 2011 2012 2010 2011 2012 2010 2011 2012
Jan 0 15363 0 0 18384 28062 0 1160 0
Feb 0 10216 295 0 16652 1559 0 0 0
March 0 0 0 0 51133 28411 0 0 0
April 0 0 37 0 20328 22632 0 5275 992
May 22960 0 0 0 8359 0 0 0 0
June 7418 22960 0 19429 15407 771 700 7670 18
July 6052 0 0 0 19523 0 0 0 8719
Aug 7926 0 0 13100 0 0 0 7670 17515
Sept 6134 6095 0 15882 18992 0 0 22185 0
Oct 11242 3640 3875 27605 0 0 0 0 15582
Nov 18649 4644 0 22328 32070 0 0 0 10535
Dec 21152 2374 0 9676 12657 0 0 0 0
Forecasts Using Double Exponential Smoothing for 20 litres Paint Bucket
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
191 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Data 20 litres Paint Bucket
Length 36
Smoothing Constants
Alpha (level) 0.695210
Gamma (trend) 0.050160
Accuracy Measures
MAPE 102
MAD 4498
MSD 52991952
20 litres
Paint
Time Bucket Smooth Predict Error
1 0 346.4 1136.7 -1136.7
2 0 364.9 1197.2 -1197.2
3 0 383.7 1258.9 -1258.9
4 0 401.5 1317.2 -1317.2
5 22960 16354.9 1289.1 21670.9
6 7418 10642.7 17998.2 -10580.2
7 6052 7839.6 11917.1 -5865.1
8 7926 8225.7 8909.4 -983.4
9 6134 7087.2 9261.3 -3127.3
10 11242 10258.0 8013.6 3228.4
11 18649 16408.2 11297.1 7351.9
12 21152 20101.0 17703.6 3448.4
13 15363 17238.6 21516.6 -6153.6
14 10216 12722.5 18439.7 -8223.7
15 0 4156.4 13636.8 -13636.8
16 0 1400.5 4595.1 -4595.1
17 0 511.8 1679.1 -1679.1
18 22960 16185.1 731.8 22228.2
19 0 5236.3 17180.2 -17180.2
20 0 1716.7 5632.4 -5632.4
21 6095 4821.4 1916.3 4178.7
22 3640 4105.3 5166.7 -1526.7
23 4644 4568.8 4397.4 246.6
24 2374 3134.6 4869.5 -2495.5
25 0 1020.5 3348.3 -3348.3
26 295 545.7 1117.4 -822.4
27 0 187.1 613.9 -613.9
28 37 97.0 233.9 -196.9
29 0 41.7 137.0 -137.0
30 0 23.4 76.9 -76.9
31 0 17.0 55.9 -55.9
32 0 14.5 47.6 -47.6
33 0 13.2 43.4 -43.4
34 3875 2706.3 40.6 3834.4
35 0 874.0 2867.4 -2867.4
36 0 285.0 935.0 -935.0
Forecasts
Period Forecast Lower Upper
37 313.48 -10706 11333
38 341.96 -13531 14215
39 370.45 -16606 17347
40 398.93 -19815 20613
41 427.42 -23104 23959
42 455.90 -26442 27354
43 484.39 -29814 30783
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
192 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
44 512.87 -33210 34235
45 541.36 -36621 37704
46 569.84 -40046 41186
47 598.33 -43480 44677
48 626.81 -46922 48175
49 655.30 -50369 51680
50 683.78 -53822 55189
51 712.27 -57278 58703
52 740.75 -60738 62219
53 769.24 -64200 65739
54 797.72 -67665 69260
55 826.21 -71131 72784
56 854.69 -74600 76309
57 883.18 -78070 79836
58 911.66 -81541 83364
59 940.15 -85013 86894
60 968.63 -88487 90424
61 997.12 -91961 93955
62 1025.60 -95436 97487
63 1054.09 -98912 101020
64 1082.57 -102388 104553
65 1111.06 -105865 108087
66 1139.55 -109343 111622
67 1168.03 -112821 115157
68 1196.52 -116299 118692
69 1225.00 -119778 122228
70 1253.49 -123257 125764
71 1281.97 -126737 129301
72 1310.46 -130216 132837
9080706050403020101
200000
100000
0
-100000
-200000
Index
20
litr
es
Pa
int
Bu
cke
t
Alpha (level) 0.695210
Gamma (trend) 0.050160
Smoothing Constants
MAPE 102
MAD 4498
MSD 52991952
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
20 litres Paint BucketDouble Exponential Method
Figure 1: Double Exponential Smoothing Plot for 20 litres Paint Bucket
Forecasts Using Double Exponential Smoothing for 4 litres Paint Bucket
Data 4 litres Paint Bucket
Length 36
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
193 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Smoothing Constants
Alpha (level) 0.772032
Gamma (trend) 0.010000
Accuracy Measures
MAPE 159
MAD 10094
MSD 191180985
4 litres
Paint
Time Bucket Smooth Predict Error
1 0 3056.7 13408.3 -13408.3
2 0 644.4 2826.6 -2826.6
3 0 89.5 392.5 -392.5
4 0 -37.7 -165.5 165.5
5 0 -66.4 -291.4 291.4
6 19429 14927.4 -317.8 19746.8
7 0 3380.4 14828.4 -14828.4
8 13100 10835.6 3167.0 9933.0
9 15882 14700.4 10698.8 5183.2
10 27605 24641.1 14603.7 13001.3
11 22328 22856.1 24644.7 -2316.7
12 9676 12677.4 22841.9 -13165.9
13 18384 17056.7 12561.5 5822.5
14 16652 16728.1 16985.7 -333.7
15 51133 43273.0 16654.6 34478.4
16 20328 25602.6 43465.7 -23137.7
17 8359 12293.2 25616.7 -17257.7
18 15407 14670.0 12174.0 3233.0
19 19523 18395.2 14575.7 4947.3
20 0 4180.7 18339.1 -18339.1
21 18992 15570.5 3983.1 15008.9
22 0 3530.9 15488.7 -15488.7
23 32070 25518.1 3329.6 28740.4
24 12657 15593.6 25538.7 -12881.7
25 28062 25201.6 15514.7 12547.3
26 1559 6952.9 25219.6 -23660.6
27 28411 23481.7 6788.2 21622.8
28 22632 22826.2 23483.9 -851.9
29 0 5202.7 22821.9 -22821.9
30 771 1740.1 5022.1 -4251.1
31 0 348.1 1526.8 -1526.8
32 0 28.0 122.9 -122.9
33 0 -45.2 -198.1 198.1
34 0 -61.5 -269.7 269.7
35 0 -64.7 -284.0 284.0
36 0 -65.0 -285.0 285.0
Forecasts
Period Forecast Lower Upper
37 -283.1 -25012 24446
38 -501.1 -33108 32105
39 -719.2 -41819 40381
40 -937.3 -50833 48958
41 -1155.4 -60013 57703
42 -1373.5 -69295 66548
43 -1591.6 -78642 75459
44 -1809.7 -88033 84414
45 -2027.7 -97456 93401
46 -2245.8 -106902 102411
47 -2463.9 -116367 111439
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
194 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
48 -2682.0 -125844 120480
49 -2900.1 -135333 129533
50 -3118.2 -144830 138594
51 -3336.2 -154335 147662
52 -3554.3 -163845 156736
53 -3772.4 -173360 165815
54 -3990.5 -182879 174898
55 -4208.6 -192402 183985
56 -4426.7 -201928 193074
57 -4644.8 -211456 202167
58 -4862.8 -220987 211261
59 -5080.9 -230520 220358
60 -5299.0 -240054 229456
61 -5517.1 -249590 238556
62 -5735.2 -259127 247657
63 -5953.3 -268666 256759
64 -6171.3 -278206 265863
65 -6389.4 -287746 274968
66 -6607.5 -297288 284073
67 -6825.6 -306831 293179
68 -7043.7 -316374 302286
69 -7261.8 -325918 311394
70 -7479.9 -335462 320502
71 -7697.9 -345007 329611
72 -7916.0 -354553 338721
9080706050403020101
500000
250000
0
-250000
-500000
-750000
Index
4 lit
res P
ain
t B
ucke
t
Alpha (level) 0.772032
Gamma (trend) 0.010000
Smoothing Constants
MAPE 159
MAD 10094
MSD 191180985
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
4 litres Paint BucketDouble Exponential Method
Figure 2: Double Exponential Smoothing Plot for 4 litres Paint Bucket
Double Exponential Smoothing for Dust Pan (Parker)
Data Dust Pan (Parker)
Length 36
Smoothing Constants
Alpha (level) 0.520489
Gamma (trend) 0.089912
Accuracy Measures
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
195 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
MAPE 76
MAD 3955
MSD 37498660
Dust Pan
Time (Parker) Smooth Predict Error
1 0 312.6 651.8 -651.8
2 0 363.1 757.2 -757.2
3 0 432.9 902.9 -902.9
4 0 536.4 1118.6 -1118.6
5 0 560.9 1169.7 -1169.7
6 700 910.7 1139.5 -439.5
7 0 704.3 1468.8 -1468.8
8 0 572.3 1193.6 -1193.6
9 0 482.3 1005.8 -1005.8
10 0 416.5 868.7 -868.7
11 0 365.5 762.3 -762.3
12 0 323.9 675.6 -675.6
13 1160 892.6 602.4 557.6
14 0 574.0 1197.1 -1197.1
15 0 394.4 822.5 -822.5
16 5275 3035.4 604.4 4670.6
17 0 1661.0 3464.0 -3464.0
18 7670 4916.4 1927.5 5742.5
19 0 2614.1 5451.6 -5451.6
20 7670 5380.0 2894.2 4775.8
21 22185 14368.3 5883.5 16301.5
22 0 7497.0 15634.7 -15634.7
23 0 3851.4 8031.8 -8031.8
24 0 1923.0 4010.3 -4010.3
25 0 908.3 1894.2 -1894.2
26 0 379.2 790.9 -790.9
27 0 107.8 224.8 -224.8
28 992 488.9 -57.1 1049.1
29 0 178.9 373.1 -373.1
30 18 31.2 45.6 -27.6
31 8719 4488.6 -103.4 8822.4
32 17515 11402.1 4766.9 12748.1
33 0 5887.0 12277.0 -12277.0
34 15582 11077.1 6187.3 9394.7
35 10535 11149.8 11817.1 -1282.1
36 0 5672.5 11829.8 -11829.8
Forecasts
Period Forecast Lower Upper
37 5798.9 -3891 15489
38 5925.3 -5172 17022
39 6051.6 -6597 18701
40 6178.0 -8121 20477
41 6304.4 -9712 22321
42 6430.7 -11352 24213
43 6557.1 -13026 26141
44 6683.5 -14727 28094
45 6809.8 -16448 30068
46 6936.2 -18185 32057
47 7062.6 -19934 34059
48 7189.0 -21693 36070
49 7315.3 -23460 38090
50 7441.7 -25233 40117
51 7568.1 -27013 42149
52 7694.4 -28797 44186
53 7820.8 -30585 46227
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
196 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
54 7947.2 -32377 48271
55 8073.6 -34172 50319
56 8199.9 -35969 52369
57 8326.3 -37768 54421
58 8452.7 -39570 56475
59 8579.0 -41373 58531
60 8705.4 -43178 60589
61 8831.8 -44985 62648
62 8958.2 -46792 64708
63 9084.5 -48601 66770
64 9210.9 -50410 68832
65 9337.3 -52221 70896
66 9463.6 -54032 72960
67 9590.0 -55845 75025
68 9716.4 -57658 77090
69 9842.8 -59471 79157
70 9969.1 -61285 81224
71 10095.5 -63100 83291
72 10221.9 -64915 85359
9080706050403020101
150000
100000
50000
0
-50000
-100000
Index
Du
st
Pa
n (
Pa
rke
r)
Alpha (level) 0.520489
Gamma (trend) 0.089912
Smoothing Constants
MAPE 76
MAD 3955
MSD 37498660
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
Dust Pan (Parker)Double Exponential Method
Figure 3: Double Exponential Smoothing Plot for Dust Pan (Parker)
Table 2: Quarterly Production Demand Data
Year Quarterly 20 litres Paint Bucket 4 litres Paint Bucket Dust Pan (Parker)
2010 Quarter:1 0 0 0
Quarter:2 30378 19429 700
Quarter:3 20112 28982 0
Quarter:4 51043 59609 0
2011 Quarter:1 25579 86169 1160
Quarter:2 22960 44094 12945
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
197 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
Quarter:3 6095 38515 29855
Quarter:4 10658 44727 0
2012 Quarter:1 295 58032 0
Quarter:2 37 23403 1010
Quarter:3 0 0 26234
Quarter:4 3875 0 26117
Winters' Method for 20 litres Paint Bucket (Quartly Multiplicative Method
Data 20 litres Paint Bucket (Quartly
Length 12
Smoothing Constants
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Accuracy Measures
MAPE 12014
MAD 31868
MSD 1404629720
Forecasts
Period Forecast Lower Upper
13 16412.0 -61663 94487
14 20591.4 -58707 99889
15 13135.6 -67526 93797
16 14539.5 -67620 96699
17 18149.2 -65634 101933
18 11513.6 -74013 97040
19 12667.0 -74715 100049
20 15707.0 -73636 105050
21 9891.5 -81510 101293
22 10794.5 -82758 104347
23 13264.8 -82525 109054
24 8269.5 -89836 106375
25 8922.0 -91575 109419
26 10822.6 -92134 113779
27 6647.4 -98833 112128
28 7049.6 -101015 115114
29 8380.4 -102324 119085
30 5025.4 -108371 118421
31 5177.1 -110959 121313
32 5938.2 -112982 124858
33 3403.3 -118343 125149
34 3304.6 -121306 127916
35 3496.0 -124016 131008
36 1781.3 -128666 132229
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
198 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
3632282420161284
150000
100000
50000
0
-50000
-100000
Index
20
litr
es
Pa
int
Bu
cke
t (Q
ua
rtly
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Smoothing Constants
MAPE 12014
MAD 31868
MSD 1404629720
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
Winters' Method Plot for 20 litres Paint Bucket (QuartlyMultiplicative Method
Figure 4: Winters' Method Plot for 20 litres Paint Bucket (Quartly
Winters' Method for 4 litres Paint Bucket(Quartly)
Multiplicative Method
Data 4 litres Paint Bucket(Quartly)
Length 12
Smoothing Constants
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Accuracy Measures
MAPE 84
MAD 38483
MSD 2504528758
Forecasts
Period Forecast Lower Upper
13 42482.9 -51799 136765
14 54453.6 -41305 150213
15 44728.8 -52677 142135
16 42853.4 -56361 142068
17 54927.2 -46248 156103
18 45116.7 -58164 148397
19 43224.0 -62297 148745
20 55400.7 -52488 163290
21 45504.5 -64871 155880
22 43594.5 -69378 156567
23 55874.3 -59799 171548
24 45892.4 -72579 164363
25 43965.0 -77393 165323
26 56347.8 -67981 180677
27 46280.3 -81097 173657
28 44335.5 -86162 174833
29 56821.4 -76864 190507
30 46668.1 -90267 183603
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
199 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
31 44706.1 -95537 184950
32 57294.9 -86311 200901
33 47056.0 -99963 194075
34 45076.6 -105402 195555
35 57768.5 -96213 211750
36 47443.8 -110083 204970
3632282420161284
250000
200000
150000
100000
50000
0
-50000
-100000
Index
4 lit
res P
ain
t B
ucke
t(Q
ua
rtly
)
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Smoothing Constants
MAPE 84
MAD 38483
MSD 2504528758
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
Winters' Method Plot for 4 litres Paint Bucket(Quartly)Multiplicative Method
Figure 5: Winters' Method Plot for 4 litres Paint Bucket(Quartly)
Winters' Method for Dust Pan (Parker) (Quartly)
Multiplicative Method
Data Dust Pan (Parker) (Quartly)
Length 12
Smoothing Constants
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Accuracy Measures
MAPE 215
MAD 10279
MSD 210732234
Forecasts
Period Forecast Lower Upper
13 12494.6 -12688.9 37678
14 28468.0 2890.0 54046
15 25419.6 -598.4 51438
16 15606.6 -10894.3 42108
17 35015.0 7990.1 62040
18 30849.2 3262.1 58436
19 18718.7 -9467.0 46904
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
200 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
20 41561.9 12743.9 70380
21 36278.9 6796.7 65761
22 21830.7 -8345.3 52007
23 48108.9 17211.4 79006
24 41708.5 10063.9 73353
25 24942.7 -7473.1 57358
26 54655.8 21446.5 87865
27 47138.2 13114.7 81162
28 28054.7 -6802.3 62912
29 61202.7 25494.3 96911
30 52567.8 15991.3 89144
31 31166.7 -6293.5 68627
32 67749.7 29391.3 106108
33 57997.5 18727.6 97267
34 34278.8 -5915.3 74473
35 74296.6 33166.8 115427
36 63427.2 21350.5 105504
3632282420161284
125000
100000
75000
50000
25000
0
Index
Du
st P
an
(P
ark
er)
(Q
ua
rtly
)
Alpha (level) 0.2
Gamma (trend) 0.2
Delta (seasonal) 0.2
Smoothing Constants
MAPE 215
MAD 10279
MSD 210732234
Accuracy Measures
Actual
Fits
Forecasts
95.0% PI
Variable
Winters' Method Plot for Dust Pan (Parker) (Quartly)Multiplicative Method
Figure 6: Winters' Method Plot for Dust Pan (Parker) (Quartly)
Discussion of Results: The discussion was based on the analysis and the results developed. The
data were analyzed with a statistical tool called Mintab and forecasting tools namely; Double
Exponential Smoothing and Winters’ methods of forecasting. From the analysis, the researcher
observed that the 20 litres Paint Bucket and Dust Pan (parker) forecasts were increasing over the
months while the 4 litres Paint Bucket forecast was decreasing. For the quarterly forecasts, the
Winters’ forecasting method was used to forecast their production demand. Furthermore, the
model shows that the Dust Pan (parker) production demand increases quarterly as was shown in
figure 6 above. However, the 4 litres Paint Bucket result shows that the forecasted production
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
201 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
demand were almost suitable while the 20 litres Paint Bucket were decreasing. This results show
that there is a need to always analyze their production yield in other to understand the current
status or problems of the production activities facing the company.
Conclusion: The result analysis and the discussion of the results have made us to understand the
current status and problems of the company. This shows that the company has to make some
serious decisions either to stop the production of the products that were going to decrease in the
future or to reorganize their activities in other to compete better in the competitive market or
improve in the quality of their products during production planning.
REFERENCES
1. Scott Armstrong, Fred Collopy, Andreas Graefe and Kesten C. Green. "Answers to Frequently
Asked Questions". Retrieved May 15, 2013.
2. Nahmias, Steven (2009). Production and Operations Analysis.
3. Ellis, Kimberly (2008). Production Planning and Inventory Control Virginia Tech. McGraw Hill.
ISBN 978-0-390-87106-0.
4. J. Scott Armstrong and Fred Collopy (1992). "Error Measures For Generalizing About Forecasting
Methods: Empirical Comparisons". International Journal of Forecasting 8: 69–80.
5. J. Scott Armstrong, Kesten C. Green and Andreas Graefe (2010). "Answers to Frequently Asked
Questions".
6. Kesten C. Greene and J. Scott Armstrong (2007). "The Ombudsman: Value of Expertise for
Forecasting Decisions in Conflicts". Interfaces (INFORMS) 0: 1–12.
7. Kesten C. Green and J. Scott Armstrong (1975). "Role thinking: Standing in other people’s shoes
to forecast decisions in conflicts". Role thinking: Standing in other people’s shoes to forecast
decisions in conflicts 39: 111–116.
8. "FAQ". Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
9. Kesten C. Greene and J. Scott Armstrong.
[http://www.qbox.wharton.upenn.edu/documents/mktg/research/INTFOR3581%20-
%20Publication% 2015.pdf "Structured analogies for forecasting"] (PDF).
qbox.wharton.upenn.edu.
10. "FAQ". Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
11. "Selection Tree". Forecastingprinciples.com. 1998-02-14. Retrieved 2012-08-28.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 2, February 2014
202 ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR
12. J. Scott Armstrong (1983). "Relative Accuracy of Judgmental and Extrapolative Methods in
Forecasting Annual Earnings". Journal of Forecasting 2: 437–447.
13. Cox, John D. (2002). Storm Watchers. John Wiley & Sons, Inc. pp. 222–224. ISBN 0-471-38108-X.
14. Super intelligence. Answer to the 2009 EDGE QUESTION: "WHAT WILL CHANGE EVERYTHING?":
http://www.nickbostrom.com/views/superintelligence.pdf
15. http://entranceguruji.in/read_matirial.php?maincourse=BBA-
MBA%20(Matirials%20for%20business%20Management)&subject=Fundamentals%20of%20Eco
nomics&topic=Demand%20and%20supply&subsubject=Importance%20of%20Demand%20Forec
asting